A computationally efficient approach for solving RBSC-based formulation of the subset selection problem

Abstract

This study focuses on a specific type of subset selection problem, which is constrained in terms of the rank bi-serial correlation (RBSC) coefficient of the outputs. For solving such problems, we propose an approach with several advantages such as (i) providing a clear insight into the feasibility of the problem with respect to the hyper-parameters, (ii) being non-iterative, (iii) having a foreseeable running time, and (iv) with the potential to yield non-deterministic (diverse) outputs. In particular, the proposed approach is based on starting from a composition of subsets with an extreme value of the RBSC coefficient (e.g. ρ=1) and swapping certain elements of the subsets in order to adjust ρ into the desired range. The proposed method is superior to the previously proposed RBSC-SubGen, which attempts to solve the problem before confirming its feasibility, taking random steps, and has unforeseeable running times and saturation issues.